A biochemical description of postsynaptic plasticity-with timescales ranging from milliseconds to seconds.

Synaptic plasticity [long-term potentiation/depression (LTP/D)], is a cellular mechanism underlying learning. Two distinct types of early LTP/D (E-LTP/D), acting on very different time scales, have been observed experimentally-spike timing dependent plasticity (STDP), on time scales of tens of ms; and behavioral time scale synaptic plasticity (BTSP), on time scales of seconds. BTSP is a candidate for a mechanism underlying rapid learning of spatial location by place cells. Here, a computational model of the induction of E-LTP/D at a spine head of a synapse of a hippocampal pyramidal neuron is developed. The single-compartment model represents two interacting biochemical pathways for the activation (phosphorylation) of the kinase (CaMKII) with a phosphatase, with ion inflow through channels (NMDAR, CaV1,Na). The biochemical reactions are represented by a deterministic system of differential equations, with a detailed description of the activation of CaMKII that includes the opening of the compact state of CaMKII. This single model captures realistic responses (temporal profiles with the differing timescales) of STDP and BTSP and their asymmetries. The simulations distinguish several mechanisms underlying STDP vs. BTSP, including i) the flow of [Formula: see text] through NMDAR vs. CaV1 channels, and ii) the origin of several time scales in the activation of CaMKII. The model also realizes a priming mechanism for E-LTP that is induced by [Formula: see text] flow through CaV1.3 channels. Once in the spine head, this small additional [Formula: see text] opens the compact state of CaMKII, placing CaMKII ready for subsequent induction of LTP.

Endocannabinoid-dependent formation of columnar axonal projection in the mouse cerebral cortex.

Columnar structure is one of the most fundamental morphological features of the cerebral cortex and is thought to be the basis of information processing in higher animals. Yet, how such a topographically precise structure is formed is largely unknown. Formation of columnar projection of layer 4 (L4) axons is preceded by thalamocortical formation, in which type 1 cannabinoid receptors (CB1R) play an important role in shaping barrel-specific targeted projection by operating spike timing-dependent plasticity during development (Itami et al., J. Neurosci. 36, 7039-7054 [2016]; Kimura & Itami, J. Neurosci. 39, 3784-3791 [2019]). Right after the formation of thalamocortical projections, CB1Rs start to function at L4 axon terminals (Itami & Kimura, J. Neurosci. 32, 15000-15011 [2012]), which coincides with the timing of columnar shaping of L4 axons. Here, we show that the endocannabinoid 2-arachidonoylglycerol (2-AG) plays a crucial role in columnar shaping. We found that L4 axon projections were less organized until P12 and then became columnar after CB1Rs became functional. By contrast, the columnar organization of L4 axons was collapsed in mice genetically lacking diacylglycerol lipase α, the major enzyme for 2-AG synthesis. Intraperitoneally administered CB1R agonists shortened axon length, whereas knockout of CB1R in L4 neurons impaired columnar projection of their axons. Our results suggest that endocannabinoid signaling is crucial for shaping columnar axonal projection in the cerebral cortex.

Calcium-Permeable AMPA Receptors Mediate Timing-Dependent LTP Elicited by Low Repeat Coincident Pre- and Postsynaptic Activity at Schaffer Collateral-CA1 Synapses.

High-frequency stimulation induced long-term potentiation (LTP) and low-frequency stimulation induced LTD are considered as cellular models of memory formation. Interestingly, spike timing-dependent plasticity (STDP) can induce equally robust timing-dependent LTP (t-LTP) and t-LTD in response to low frequency repeats of coincident action potential (AP) firing in presynaptic and postsynaptic cells. Commonly, STDP paradigms relying on 25-100 repeats of coincident AP firing are used to elicit t-LTP or t-LTD, but the minimum number of repeats required for successful STDP is barely explored. However, systematic investigation of physiologically relevant low repeat STDP paradigms is of utmost importance to explain learning mechanisms in vivo. Here, we examined low repeat STDP at Schaffer collateral-CA1 synapses by pairing one presynaptic AP with either one postsynaptic AP (1:1 t-LTP), or a burst of 4 APs (1:4 t-LTP) and found 3-6 repeats to be sufficient to elicit t-LTP. 6× 1:1 t-LTP required postsynaptic Ca2+ influx via NMDARs and L-type VGCCs and was mediated by increased presynaptic glutamate release. In contrast, 1:4 t-LTP depended on postsynaptic metabotropic GluRs and ryanodine receptor signaling and was mediated by postsynaptic insertion of AMPA receptors. Unexpectedly, both 6× t-LTP variants were strictly dependent on activation of postsynaptic Ca2+-permeable AMPARs but were differentially regulated by dopamine receptor signaling. Our data show that synaptic changes induced by only 3-6 repeats of mild STDP stimulation occurring in ≤10 s can take place on time scales observed also during single trial learning.

Synaptic plasticity rules with physiological calcium levels.

Spike-timing-dependent plasticity (STDP) is considered as a primary mechanism underlying formation of new memories during learning. Despite the growing interest in activity-dependent plasticity, it is still unclear whether synaptic plasticity rules inferred from in vitro experiments are correct in physiological conditions. The abnormally high calcium concentration used in in vitro studies of STDP suggests that in vivo plasticity rules may differ significantly from in vitro experiments, especially since STDP depends strongly on calcium for induction. We therefore studied here the influence of extracellular calcium on synaptic plasticity. Using a combination of experimental (patch-clamp recording and Ca2+ imaging at CA3-CA1 synapses) and theoretical approaches, we show here that the classic STDP rule in which pairs of single pre- and postsynaptic action potentials induce synaptic modifications is not valid in the physiological Ca2+ range. Rather, we found that these pairs of single stimuli are unable to induce any synaptic modification in 1.3 and 1.5 mM calcium and lead to depression in 1.8 mM. Plasticity can only be recovered when bursts of postsynaptic spikes are used, or when neurons fire at sufficiently high frequency. In conclusion, the STDP rule is profoundly altered in physiological Ca2+, but specific activity regimes restore a classical STDP profile.

Synchronization in STDP-driven memristive neural networks with time-varying topology.

Synchronization is a widespread phenomenon in the brain. Despite numerous studies, the specific parameter configurations of the synaptic network structure and learning rules needed to achieve robust and enduring synchronization in neurons driven by spike-timing-dependent plasticity (STDP) and temporal networks subject to homeostatic structural plasticity (HSP) rules remain unclear. Here, we bridge this gap by determining the configurations required to achieve high and stable degrees of complete synchronization (CS) and phase synchronization (PS) in time-varying small-world and random neural networks driven by STDP and HSP. In particular, we found that decreasing P (which enhances the strengthening effect of STDP on the average synaptic weight) and increasing F (which speeds up the swapping rate of synapses between neurons) always lead to higher and more stable degrees of CS and PS in small-world and random networks, provided that the network parameters such as the synaptic time delay [Formula: see text], the average degree [Formula: see text], and the rewiring probability [Formula: see text] have some appropriate values. When [Formula: see text], [Formula: see text], and [Formula: see text] are not fixed at these appropriate values, the degree and stability of CS and PS may increase or decrease when F increases, depending on the network topology. It is also found that the time delay [Formula: see text] can induce intermittent CS and PS whose occurrence is independent F. Our results could have applications in designing neuromorphic circuits for optimal information processing and transmission via synchronization phenomena.

[A bio-inspired hierarchical spiking neural network with biological synaptic plasticity for event camera object recognition].

With inherent sparse spike-based coding and asynchronous event-driven computation, spiking neural network (SNN) is naturally suitable for processing event stream data of event cameras. In order to improve the feature extraction and classification performance of bio-inspired hierarchical SNNs, in this paper an event camera object recognition system based on biological synaptic plasticity is proposed. In our system input event streams were firstly segmented adaptively using spiking neuron potential to improve computational efficiency of the system. Multi-layer feature learning and classification are implemented by our bio-inspired hierarchical SNN with synaptic plasticity. After Gabor filter-based event-driven convolution layer which extracted primary visual features of event streams, we used a feature learning layer with unsupervised spiking timing dependent plasticity (STDP) rule to help the network extract frequent salient features, and a feature learning layer with reward-modulated STDP rule to help the network learn diagnostic features. The classification accuracies of the network proposed in this paper on the four benchmark event stream datasets were better than the existing bio-inspired hierarchical SNNs. Moreover, our method showed good classification ability for short event stream input data, and was robust to input event stream noise. The results show that our method can improve the feature extraction and classification performance of this kind of SNNs for event camera object recognition.

Weight dependence in BCM leads to adjustable synaptic competition.

Models of synaptic plasticity have been used to better understand neural development as well as learning and memory. One prominent classic model is the Bienenstock-Cooper-Munro (BCM) model that has been particularly successful in explaining plasticity of the visual cortex. Here, in an effort to include more biophysical detail in the BCM model, we incorporate 1) feedforward inhibition, and 2) the experimental observation that large synapses are relatively harder to potentiate than weak ones, while synaptic depression is proportional to the synaptic strength. These modifications change the outcome of unsupervised plasticity under the BCM model. The amount of feed-forward inhibition adds a parameter to BCM that turns out to determine the strength of competition. In the limit of strong inhibition the learning outcome is identical to standard BCM and the neuron becomes selective to one stimulus only (winner-take-all). For smaller values of inhibition, competition is weaker and the receptive fields are less selective. However, both BCM variants can yield realistic receptive fields.

A TSTDP memristive synapse based on a comprehensive mathematical model of memory-TFT threshold voltage shift.

New emerging nano-scale technologies like hydrogenated noncrystalline-silicon thin-film transistors (TFTs) and memristors, fabricated at low temperatures and over large areas, permit low-cost processing and 3D integration with CMOS cores. Here, we aim to propose a mathematical model which explains the memory-TFT threshold voltage shift due to the gate bias instability. Then, based on this mathematical approach, we propose a novel learning synapse composed of a voltage/flux driven memristor in parallel with a common-source memory-TFT with a memristive load. The proposed device realizes the triplet-based spike-timing-dependent plasticity rule (TSTDP) as a more realistic form of learning than the purely pair-based STDP (PSTDP). PSTDP is a synaptic learning rule which utilizes a constant-frequency pairing protocol to induce synaptic weight change and cannot explain the modification due to the frequency changes of spike pairs, and also the outcomes of triplet and quadruplet experiments. However, TSTDP improves the learning capabilities of the conventional PSTDP and reproduces the results of more electrophysiological experiments. In this paper, we apply various spike patterns like different-frequency and different-timing spike pairs, spike triplets, and quadruplets to the proposed device. Our simulations confirm a close match with the experimental data sets of real biological synapses.

A CMOS-memristor hybrid system for implementing stochastic binary spike timing-dependent plasticity.

This paper describes a fully experimental hybrid system in which a [Formula: see text] memristive crossbar spiking neural network (SNN) was assembled using custom high-resistance state memristors with analogue CMOS neurons fabricated in 180 nm CMOS technology. The custom memristors used NMOS selector transistors, made available on a second 180 nm CMOS chip. One drawback is that memristors operate with currents in the micro-amperes range, while analogue CMOS neurons may need to operate with currents in the pico-amperes range. One possible solution was to use a compact circuit to scale the memristor-domain currents down to the analogue CMOS neuron domain currents by at least 5-6 orders of magnitude. Here, we proposed using an on-chip compact current splitter circuit based on MOS ladders to aggressively attenuate the currents by over 5 orders of magnitude. This circuit was added before each neuron. This paper describes the proper experimental operation of an SNN circuit using a [Formula: see text] 1T1R synaptic crossbar together with four post-synaptic CMOS circuits, each with a 5-decade current attenuator and an integrate-and-fire neuron. It also demonstrates one-shot winner-takes-all training and stochastic binary spike-timing-dependent-plasticity learning using this small system. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.

Transcranial Magnetic Stimulation and the Understanding of Behavior.

The development of the use of transcranial magnetic stimulation (TMS) in the study of psychological functions has entered a new phase of sophistication. This is largely due to an increasing physiological knowledge of its effects and to its being used in combination with other experimental techniques. This review presents the current state of our understanding of the mechanisms of TMS in the context of designing and interpreting psychological experiments. We discuss the major conceptual advances in behavioral studies using TMS. There are meaningful physiological and technical achievements to review, as well as a wealth of new perceptual and cognitive experiments. In doing so we summarize the different uses and challenges of TMS in mental chronometry, perception, awareness, learning, and memory.

Long-term population spike-timing-dependent plasticity promotes synaptic tagging but not cross-tagging in rat hippocampal area CA1.

In spike-timing-dependent plasticity (STDP), the direction and degree of synaptic modification are determined by the coherence of pre- and postsynaptic activities within a neuron. However, in the adult rat hippocampus, it remains unclear whether STDP-like mechanisms in a neuronal population induce synaptic potentiation of a long duration. Thus, we asked whether the magnitude and maintenance of synaptic plasticity in a population of CA1 neurons differ as a function of the temporal order and interval between pre- and postsynaptic activities. Modulation of the relative timing of Schaffer collateral fibers (presynaptic component) and CA1 axons (postsynaptic component) stimulations resulted in an asymmetric population STDP (pSTDP). The resulting potentiation in response to 20 pairings at 1 Hz was largest in magnitude and most persistent (4 h) when presynaptic activity coincided with or preceded postsynaptic activity. Interestingly, when postsynaptic activation preceded presynaptic stimulation by 20 ms, an immediate increase in field excitatory postsynaptic potentials was observed, but it eventually transformed into a synaptic depression. Furthermore, pSTDP engaged in selective forms of late-associative activity: It facilitated the maintenance of tetanization-induced early long-term potentiation (LTP) in neighboring synapses but not early long-term depression, reflecting possible mechanistic differences with classical tetanization-induced LTP. The data demonstrate that a pairing of pre- and postsynaptic activities in a neuronal population can greatly reduce the required number of synaptic plasticity-evoking events and induce a potentiation of a degree and duration similar to that with repeated tetanization. Thus, pSTDP determines synaptic efficacy in the hippocampal CA3-CA1 circuit and could bias the CA1 neuronal population toward potentiation in future events.

Role of Group I Metabotropic Glutamate Receptors in Spike Timing-Dependent Plasticity.

Metabotropic glutamate receptors (mGluRs) are G-protein-coupled receptors that exhibit enormous diversity in their expression patterns, sequence homology, pharmacology, biophysical properties and signaling pathways in the brain. In general, mGluRs modulate different traits of neuronal physiology, including excitability and plasticity processes. Particularly, group I mGluRs located at the pre- or postsynaptic compartments are involved in spike timing-dependent plasticity (STDP) at hippocampal and neocortical synapses. Their roles of participating in the underlying mechanisms for detection of activity coincidence in STDP induction are debated, and diverse findings support models involving mGluRs in STDP forms in which NMDARs do not operate as classical postsynaptic coincidence detectors. Here, we briefly review the involvement of group I mGluRs in STDP and their possible role as coincidence detectors.

Spike Timing-Dependent Plasticity with Enhanced Long-Term Depression Leads to an Increase of Statistical Complexity.

Synaptic plasticity is characterized by remodeling of existing synapses caused by strengthening and/or weakening of connections. This is represented by long-term potentiation (LTP) and long-term depression (LTD). The occurrence of a presynaptic spike (or action potential) followed by a temporally nearby postsynaptic spike induces LTP; conversely, if the postsynaptic spike precedes the presynaptic spike, it induces LTD. This form of synaptic plasticity induction depends on the order and timing of the pre- and postsynaptic action potential, and has been termed spike time-dependent plasticity (STDP). After an epileptic seizure, LTD plays an important role as a depressor of synapses, which may lead to their complete disappearance together with that of their neighboring connections until days after the event. Added to the fact that after an epileptic seizure the network seeks to regulate the excess activity through two key mechanisms: depressed connections and neuronal death (eliminating excitatory neurons from the network), LTD becomes of great interest in our study. To investigate this phenomenon, we develop a biologically plausible model that privileges LTD at the triplet level while maintaining the pairwise structure in the STPD and study how network dynamics are affected as neuronal damage increases. We find that the statistical complexity is significantly higher for the network where LTD presented both types of interactions. While in the case where the STPD is defined with purely pairwise interactions an increase is observed as damage becomes higher for both Shannon Entropy and Fisher information.

Constraint-induced intervention as an emergent phenomenon from synaptic competition in biological systems.

The principle of constraint-induced therapy is widely practiced in rehabilitation. In hemiplegic cerebral palsy (CP) with impaired contralateral corticospinal projection due to unilateral injury, function improves after imposing a temporary constraint on limbs from the less affected hemisphere. This type of partially-reversible impairment in motor control by early brain injury bears a resemblance to the experience-dependent plastic acquisition and modification of neuronal response selectivity in the visual cortex. Previously, such mechanism was modeled within the framework of BCM (Bienenstock-Cooper-Munro) theory, a rate-based synaptic modification theory. Here, we demonstrate a minimally complex yet sufficient neural network model which provides a fundamental explanation for inter-hemispheric competition using a simplified spike-based model of information transmission and plasticity. We emulate the restoration of function in hemiplegic CP by simulating the competition between cells of the ipsilateral and contralateral corticospinal tracts. We use a high-speed hardware neural simulation to provide realistic numbers of spikes and realistic magnitudes of synaptic modification. We demonstrate that the phenomenon of constraint-induced partial reversal of hemiplegia can be modeled by simplified neural descending tracts with 2 layers of spiking neurons and synapses with spike-timing-dependent plasticity (STDP). We further demonstrate that persistent hemiplegia following unilateral cortical inactivation or deprivation is predicted by the STDP-based model but is inconsistent with BCM model. Although our model is a highly simplified and limited representation of the corticospinal system, it offers an explanation of how constraint as an intervention can help the system to escape from a suboptimal solution. This is a display of an emergent phenomenon from the synaptic competition.

Spike signal transmission between modules and the predictability of spike activity in modular neuronal networks.

Modularity is a common feature of the nervous system across species and scales. Although it has been qualitatively investigated in network science, very little is known about how it affects spike signal transmission in neuronal networks at the mesoscopic level. Here, a neuronal network model is built to simulate dynamic interactions among different modules of neuronal networks. This neuronal network model follows the organizational principle of modular structure. The neurons can generate spikes like biological neurons, and changes in the strength of synaptic connections conform to the STDP learning rule. Based on this neuronal network model, we first quantitatively studied whether and to what extent the connectivity within and between modules can affect spike signal transmission, and found that spike signal transmission heavily depends on the connectivity between modules, but has little to do with the connectivity within modules. More importantly, we further found that the spike activity of a module can be predicted according to the spike activities of its adjacent modules through building a resting-state functional connectivity matrix.

The nm-thickness Cu2-xSe ultrathin film via atomic layer deposition and its memristive effect.

The ultrathin film of copper selenide with 50 nm in thickness by the home-made atomic layer deposition apparatus was deposited. Synthesized copper pivalate and bis(triethylsilyl) selenide precursors were used. The deposition rate at 160oC was 0.48 Å per ALD cycle and the thickness was monitored by the in-situ ellipsometer and further analyzed by an AFM. The composition and structure of the film were found by XPS, Raman and XRD to be Cu1.16Se. The FTO/Cu1.16Se/tungsten wire memristor was fabricated and its memristive effect was investigated. The non-linear I - V curve and spike timing dependent plasticity of our Cu1.16Se memristor demonstrate that short-term potentiation and long-term potentiation occurring in a human brain can be realized by adjusting voltage pulse intervals. A memristor is the electrical equivalent to a synapse. Our memristor shows 1 ms of switching time, 400 s of retention time, Roff/on=2, and the reproducibility over 1000 cycles.

STDP Forms Associations between Memory Traces in Networks of Spiking Neurons.

Memory traces and associations between them are fundamental for cognitive brain function. Neuron recordings suggest that distributed assemblies of neurons in the brain serve as memory traces for spatial information, real-world items, and concepts. However, there is conflicting evidence regarding neural codes for associated memory traces. Some studies suggest the emergence of overlaps between assemblies during an association, while others suggest that the assemblies themselves remain largely unchanged and new assemblies emerge as neural codes for associated memory items. Here we study the emergence of neural codes for associated memory items in a generic computational model of recurrent networks of spiking neurons with a data-constrained rule for spike-timing-dependent plasticity. The model depends critically on 2 parameters, which control the excitability of neurons and the scale of initial synaptic weights. By modifying these 2 parameters, the model can reproduce both experimental data from the human brain on the fast formation of associations through emergent overlaps between assemblies, and rodent data where new neurons are recruited to encode the associated memories. Hence, our findings suggest that the brain can use both of these 2 neural codes for associations, and dynamically switch between them during consolidation.

Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing.

This work presents a new approach based on a spiking neural network for sound preprocessing and classification. The proposed approach is biologically inspired by the biological neuron's characteristic using spiking neurons, and Spike-Timing-Dependent Plasticity (STDP)-based learning rule. We propose a biologically plausible sound classification framework that uses a Spiking Neural Network (SNN) for detecting the embedded frequencies contained within an acoustic signal. This work also demonstrates an efficient hardware implementation of the SNN network based on the low-power Spike Continuous Time Neuron (SCTN). The proposed sound classification framework suggests direct Pulse Density Modulation (PDM) interfacing of the acoustic sensor with the SCTN-based network avoiding the usage of costly digital-to-analog conversions. This paper presents a new connectivity approach applied to Spiking Neuron (SN)-based neural networks. We suggest considering the SCTN neuron as a basic building block in the design of programmable analog electronics circuits. Usually, a neuron is used as a repeated modular element in any neural network structure, and the connectivity between the neurons located at different layers is well defined. Thus, generating a modular Neural Network structure composed of several layers with full or partial connectivity. The proposed approach suggests controlling the behavior of the spiking neurons, and applying smart connectivity to enable the design of simple analog circuits based on SNN. Unlike existing NN-based solutions for which the preprocessing phase is carried out using analog circuits and analog-to-digital conversion, we suggest integrating the preprocessing phase into the network. This approach allows referring to the basic SCTN as an analog module enabling the design of simple analog circuits based on SNN with unique inter-connections between the neurons. The efficiency of the proposed approach is demonstrated by implementing SCTN-based resonators for sound feature extraction and classification. The proposed SCTN-based sound classification approach demonstrates a classification accuracy of 98.73% using the Real-World Computing Partnership (RWCP) database.

Spatial Memory in a Spiking Neural Network with Robot Embodiment.

Cognitive maps and spatial memory are fundamental paradigms of brain functioning. Here, we present a spiking neural network (SNN) capable of generating an internal representation of the external environment and implementing spatial memory. The SNN initially has a non-specific architecture, which is then shaped by Hebbian-type synaptic plasticity. The network receives stimuli at specific loci, while the memory retrieval operates as a functional SNN response in the form of population bursts. The SNN function is explored through its embodiment in a robot moving in an arena with safe and dangerous zones. We propose a measure of the global network memory using the synaptic vector field approach to validate results and calculate information characteristics, including learning curves. We show that after training, the SNN can effectively control the robot's cognitive behavior, allowing it to avoid dangerous regions in the arena. However, the learning is not perfect. The robot eventually visits dangerous areas. Such behavior, also observed in animals, enables relearning in time-evolving environments. If a dangerous zone moves into another place, the SNN remaps positive and negative areas, allowing escaping the catastrophic interference phenomenon known for some AI architectures. Thus, the robot adapts to changing world.

Hebbian and non-Hebbian timing-dependent plasticity in the hippocampal CA3 region.

The timing between synaptic inputs has been proposed to play a role in the induction of plastic changes that enable neural circuits to store information. In the case of spike timing-dependent plasticity (STDP), this relates to the interval between a synaptic input and a postsynaptic spike, thus providing a conceptual link to the Hebb learning rule. Experiments have documented STDP in many synapses and brain regions, and computational models have tested its utility in many neural network functions. However, questions remain about whether timing plays a role in plasticity during natural activity, and whether it can function in information storage. The present study used imaging with voltage sensitive dye to investigate the effectiveness of input timing in the plasticity of responses in the CA3 region of hippocampal slices. Plasticity was induced by sequential dual-site stimulation at 10 ms intervals of either synaptic inputs and cell bodies (synaptic-somatic induction) or of two sets of synaptic inputs (synaptic-synaptic induction). Both protocols potentiated responses, with greater potentiation of responses to the first stimulation of the sequence than the second. Neither of these protocols induced depression. Synaptic-somatic stimulation was much more effective than synaptic-synaptic stimulation in evoking somatic action potentials, but both protocols potentiated responses equally well. This suggests that sequential dual-site stimulation can potentiate equally well with very different degrees of somatic action potential firing. With synaptic-somatic induction, potentiation was focused at the sites of stimulation. In contrast, with synaptic-synaptic induction, the distribution of potentiation varied greatly. Changes in the spatial distribution of responses indicated that sequential dual-site stimulation functions poorly in the storage of activity patterns. These results suggest that in the hippocampal CA3 region, timed sequential activation of two inputs is less effective than theta bursts, both in the induction of LTP and in the storage of information.